Machine Learning , some thoughts

Bert Verhees bert.verhees at rosa.nl
Sat Jun 23 12:11:59 EDT 2018


Today my wife showed me Plantnet.

https://plantnet.org/en/

It recognizes over 6000 plants from showing a flower or a leaf to your 
phone. It has learned from machine-learning 700.000 pictures, and its 
knowledge every day grows stronger, because it keeps on learning. And 
not only the looks of a flower, but if it takes location (biotope) and 
date in consideration, the certainty of recognizing gets stronger.

Now you can imagine that it must be hard to recognize a plant from a 
picture, without seeing the dimensions and showed in many possible 
angles, in sunlight, cloudy or twilight.

I was impressed how good it already was. Very advanced 
computer-knowledge for free in the hands of the millions.

There is also an app, I did not try it, which recognizes birds from 
audio. You walk somewhere, hear a bird and want to know what kind of 
bird that is.

The Berlin Natural History Museum leads a contest of 29 teams using 23 
different methods, with more than 82% good identifications for isolated 
bird recordings, and more than 74% correct identifications for 
recordings mixing several bird songs.


I often notice there is a trend in thinking that Machine Learning cannot 
be much help, see how miserable google-translate translates. But then we 
for get to see how much progress is made in other areas.

Why am I writing this? Just to let you think about it.

I wonder, Is OpenEhr usable for recognizing pattern in diseases over 
Machine Learning, isn't behind every diagnosis a small cloud of 
archetypes which forms a pattern? The features of recognizing/learning 
should not be found in archetypes ID's, although, that can help a lot, 
but it should also look to datatypes, their semantics and relations.

Isn't OpenEhr better for recognizing pattern then whichever classic 
storage structure, because the data-structures in OpenEhr are in 
semantic models, this instead of some weird Codd-structure, which only 
has technical reasons to exist.

(Classic data stored in classic SQL schema's could be brought over to 
archetyped structures, to make the base of machine-learning larger.)

I think, when this is developed, we should be able to get to at least 
two advantages.

1) We don't need CKM anymore, computers can understand archetypes, we 
don't need to restrict ourselves to a limited number. We can also use 
archetypes we do not know, and maybe we never know. Even, we wouldn't 
need archetypes anymore, just as reminder/instruction. But the computer 
could create the archetypes on the fly, when seeing the kind of data, 
the relations, the diagnosis.

2) We could use the pattern to recognize healthcare situations, and 
maybe treat/handle/cure on base of instructions coming from machine 
learning.

Some thoughts when walking with my wife through the wonderful dunes, and 
its special vegetation. Maybe I must write a blog about it.

Have a nice day.

Bert





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